A recovery boiler is a major component in the recovery cycle of black liquor, that is formed during a digester process and other pulp making processes. The black liquor contains dissolved organic compounds (from wood) and inorganic compounds (NaOH, Na2S, Na2CO3 and Na2SO4). Na2S and NaOH are chemicals used for the pulp making process in a digester. Na2CO3 and Na2SO4 are undesirable chemical species. A recovery boiler recovers Na2S from Na2SO4 via reduction reactions at the bottom of the recovery boiler furnace. NaOH is recovered from Na2CO3 in a causticizing process subsequent to the recovery boiler. The recovered stream from the recovery boiler and causticizer contains NaOH and Na2S as major species and is fed back to the digester for pulp making process.
To summarize, a recovery boiler is used to: (i) recover inorganic cooking chemicals; (ii) generate heat energy by burning the organic materials derived from the wood; and (iii) burn the organic chemicals in order not to discharge them from the mill, as pollutants. Through the recovery boiler process, the pulp mill saves on chemical cost and hence the recovery boiler (and also causticizer) can increase the economic performance of the pulp mill.
The chemical recovery boiler is a major component of the liquor cycle in a pulp mill and an important key to overall mill economic performance. Several issues add to the importance and complexity of recovery boiler operations. The variations in the calorific value (Btu content) and the temperature of black liquor, the size of the black liquor droplets, temperature and the distribution of combustion air can result in the varied performance of the boiler that in turn can affect the quality of the steam generated and the emissions from the boiler. The intensely coupled phenomena taking place inside the recovery boiler make it more difficult to operate the process at an optimum. The optimum performance of the boiler implies maximum reduction efficiency to recover the cooking chemicals, reduced emissions from the boiler and maintaining the steam quality at the desired level. Thus, it would be desirable to develop a method that considers all the above factors to ensure optimum performance of the boiler.
A modern day pulp mill uses sophisticated control systems, for example, a Distributed Control System (DCS), to regulate and optimize various processes related to the pulp mill. The DCS can also be used to optimize operations and production involved in the manufacture of pulp and paper. The control and optimization strategies can be based on modeling and simulation modules available with the DCS. A variety of process models are known from the literature, including models for a recovery boiler and mainly new modules based on these models that are available as software/hardware (mostly as software) solutions for the DCS positioned for pulp and paper mills.
Process models for a recovery boiler as known from the literature are either first principle based models or data driven models. These models are used for off-line simulations and, for control and optimization applications. The models used for control and optimization applications need to be mathematically simple enough to guarantee convergence and at the same time mathematically complex enough to capture the important dynamics and relationships among the required process variables. The data driven models have the advantage that they are mathematically simple but the use of these models is restricted to a narrow operating region of the recovery boiler. The first principle based models have the advantage that they capture the physics of the problem and thus their model predictions are reliable for a wide range of operating conditions of the recovery boiler. Hence the first principle based models are desired if they can be formulated and utilized.
A major challenge lies in the control of a reduction process inside the recovery boiler. Direct measurements of chemicals, NaOH, Na2S, Na2CO3 and Na2SO4 are not available in the recovery boiler. Therefore, it is difficult to get a direct estimate of reduction efficiency in the recovery boiler that depends on the concentration of the above mentioned chemicals. The data driven models use an indirect measure of the reduction efficiency (i.e., the temperature of the char bed at the bottom of the furnace in the recovery boiler). But, reduction efficiency also depends on several other factors such as the availability of oxygen at the bottom of the bed, the chemical composition of the black liquor entering the furnace of recovery boiler, drying, volatilization and combustion reactions that take place during the flight of black liquor from the liquor nozzles to the char bed. All of these phenomena mentioned above affect the concentration of the chemical species (carbon, NaOH, Na2S, Na2CO3 and Na2SO4) that reach the char bed and hence affect the reduction reaction rate. Therefore, data driven models that assume char bed temperature as the measure of reduction process may be inaccurate for control of reduction process.
The inefficiency of the data driven models to accurately predict the reduction reaction rate and their inability to capture the dependence of the reduction process on the various process variables of the recovery boiler result in inefficient control and optimization of the recovery boiler process. As various coupled phenomena, such as combustion, char volatilization and reduction occur in the recovery boiler, the data driven models are not able to efficiently control and optimize the recovery boiler process. This results in poor reduction efficiency, poor combustion and higher emission of the pollutants from the recovery boiler. All these factors add to the cost of the pulp mill.
Thus, there is a strong need to develop a method and a system that can accurately predict the process variables, including the concentration of chemical species in the recovery boiler, and also efficiently control and optimize the recovery boiler process.